{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 4 (Main Text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded. Shape: (1599624, 94)\n",
      "   challenger  target    year  dispute  dispute2  target_resist   id  \\\n",
      "0         2.0    20.0  1945.0      0.0       0.0            NaN  1.0   \n",
      "1         2.0    20.0  1946.0      0.0       0.0            NaN  1.0   \n",
      "2         2.0    20.0  1947.0      0.0       0.0            NaN  1.0   \n",
      "3         2.0    20.0  1948.0      0.0       0.0            NaN  1.0   \n",
      "4         2.0    20.0  1949.0      0.0       0.0            NaN  1.0   \n",
      "\n",
      "   atopally_target  defense_target  milex_challenger  ...  size_t_num2  \\\n",
      "0              1.0             1.0        90000000.0  ...          0.0   \n",
      "1              0.0             0.0        45133984.0  ...          0.0   \n",
      "2              0.0             0.0        14315999.0  ...          0.0   \n",
      "3              0.0             0.0        10960998.0  ...          0.0   \n",
      "4              1.0             1.0        13503000.0  ...        238.0   \n",
      "\n",
      "   size_t_b  dispute_hh3  dispute2_hh3  dispute_hh4  dispute2_hh4  \\\n",
      "0       0.0          0.0           0.0          0.0           0.0   \n",
      "1       0.0          0.0           0.0          0.0           0.0   \n",
      "2       0.0          0.0           0.0          0.0           0.0   \n",
      "3       0.0          0.0           0.0          0.0           0.0   \n",
      "4       1.0          0.0           0.0          0.0           0.0   \n",
      "\n",
      "   mutiny_only  violent_only  size_only  duration_only  \n",
      "0          0.0           0.0        0.0            0.0  \n",
      "1          0.0           0.0        0.0            0.0  \n",
      "2          0.0           0.0        0.0            0.0  \n",
      "3          0.0           0.0        0.0            0.0  \n",
      "4          1.0           0.0        1.0            1.0  \n",
      "\n",
      "[5 rows x 94 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fnuMYasfkyZMD3/fHH39st912mz3zzDMJfU56z4899ph7/ebNm7v3HbuUmV7zoosusqOPPtp9Xueff7598cUXkfsaN27sRmGiderUyR5//PGE2gAAAAAkNYF47bXX7IknnrAbb7zR3n33Xbv11lvtxRdftAEDBrj7VXOmzrrmUHz44YeuA/3OO+/YiBEj3P3jxo2zDz74wAYNGuTuv+yyy+zBBx+0b7/9Ntdr1axZ09544w33s5KHK6+8MnKfSpruv/9+mzRpktWvX9+1w5ukoxKdOXPm2LPPPuteo0+fPi6pUds9XiJz77332n//+19r1KiR3X333YETfYYNG2aXXHKJFStWLKHP6tFHH7X33nvPzXf4z3/+45ZPjX6fP/30k/uclADpfbz++utWpUoVu+uuu1zy0bZtW/dvfX6exYsX28yZM+2CCy6w/PCG9QAAAIB9UsI0dOhQNwdAnV7R5OYtW7bYQw89ZLfccosbNVAHW2U+tWrVcpeXXnrJypUr5x7/66+/uvIZTYCuVq2aSyC0xNfBBx+c67VKlCjhOtBSsWJFK1u2bOS+e+65JzKB+YYbbnBn6xcuXOhGRU466SQ79thj7dBDD3X367XGjBlj8+fPz3F8JR0aHfA61ko29JhmzZoV+HPSZ/LWW2/ZAw884EZbRMnUV199leP9KQm69NJLI7dppOWaa66xdevWuQTq3HPPdQmE2idKhDRSkd/5IPrMU6ksDAAAoLBp7wmWrf//Ei1d3ysJxPr1691Z9KeeeipHGY/KcjQ5V8mDyobUAb/wwgtdx12deZUhqUxIunTp4jr76lQfdthh7n4lI1WrVs1TW6ITDm9CsLdRiTrkn3zyib399tu2ZMkS++WXX1zblKhEi/63l+Ds3LnTCoNGCnQsdfY9WgdZpVMevX8lRsOHD7dFixbZ0qVL3QiON0IiGml4+eWXbdasWS45mjhxokswAAAAEJ/6VF7fDpZwP3uvJBBe/b5Kgk488cRc9+uMudbeVXmRSoimTZvmLlpF6bzzznMTeFVu9NFHH9nXX39tX375pZu7oBIo3aflYxNVvHjuKi2dWVcbe/ToYQsWLHDzG8466yw3D0Jn+mOprfGOURi8MqfY45Us+f9/NfoMrrrqKleqpDkSWhpW8zQ0ouLRSMNRRx3lEgclSGvXrnXvCwAAAPHVq1fPypcvn+xmhE7JvZW9qKRo2bJl7hfjUZ2/JhhrYq8mJv/4449ujoTOtl977bX2/PPP2wsvvOCSBCUXOo5GHTT6oHr/K664wh0jXgKR6HwDj7ZM//zzz918AnW8RSMBKp1SudW+ohESjTh8//33bqRBdu3a5UYYvNIrjSzo5+jJ4aNHj86VeGgUQqVjSo5OPfVUlmAFAAAIoCVcy5Qpk+xmFK0EQsM+6oTH/iKOO+44Vz6jCdCa29C6dWubN2+emwStMiWd0Ve92XPPPeeGjXSbVkTSKIM3r0BlULpfx9MKQyrdUadftf9BNVuamxBd/uPnwAMPdGf533//fZfsbNiwwSUv2rdhX04e1nwNze8YPHiw29CkQYMGLmHQPhLRIzYq59LE6ho1arjVobzSsOi2KtlS8qU5FdHJRn5oxSslbQAAAEChJRBaEUiXaJoUrXkFWglJZ9Z1plyrC6nDfvHFF9vNN9/sHqfSJi3bqs6yEg0lCprvoJWRRCMTGhHQCkXq1Ktz3blzZ1d2FE/lypXdGXit/KTE5rTTTgtse/Xq1V271NEeO3asO75KhLp37+7avy/dcccd7rN6+OGH3epOWvJWO3179JmpJEklXl65kiZa33nnnW4UR0mHKBnTyINKnjRqUxBK+DRfJV4JGAAAQJh5S/Yjf4pls9ROWunatasdc8wxbh+KgtB+F5p7olWiAAAA0o1OHqtknJ2oU2QVJux7KnFSiZf2ftAoTEFp2VqN/miFLCD6jI2WQdaoFzWjiEV8IAjxgVSLDZXUkzzkDwlEmtAGfFoS9pFHHnFzJgqDJrEzERvRFA8q/wPiIT4QhPiAH2IjfEgg0oR2sAYAAAD2NmbIwte+XI0K4aCJ9VoRTddALOIDQYgP+CE2wocEAnFp7oNWhgJisTIXghAfCEJ8wA+xES6swoS4tm7dmuwmAAAAIAVXYSLdQ1za2E87YgPRdL5BO51z3gHxEB8IQnzAD7ERPiQQiGv48OG2YsWKZDcDKbjUnjYvZAMexEN8IAjxAT/ERviQQMBXyZIs0oXca2bXrVvXXQOxiA8EIT7gh9gIH3qI8LVt2zbbtGlTspuBFFOiRAnmyMAX8YEgxAdSLTbYTC5/SCDS0LBhw2zatGk2evToAh3n9NNPt5UrVxZauwAAAFJJRkaGZWVlkUTkEQlEmhk7dqw9/fTT1qJFiwIfq3fv3uxEDQAA0pLmXPTs2dPte0UCkTckEGli1apV9sADD9iMGTOsfv36BT7eI488Yi+++GLCy3kBAACgaGASdZr4+eefrVSpUjZx4kQ76qijCny83bt3W7FixQqlbQAAAEgfjECkiXbt2rlLYenevbvt3Lmz0I4HAACQqovG6CQsLOHKExIIxKUyKG3qAgAAkM6WLl1q5cqVS3YzUkLVqlUTehwJBAAAAIqsevXqWfny5ZPdjFAhgQAAAECRpRWYypQpk+xmhAqTqAEAAAAkjAQCcWk1p5IlGaACAABATvQQEdf333/vNlZJxrbyAAAA+2IjOeRPsezs7Ox8PhdprE2bNvbtt9+SQAAAgLSVkZFhWVlZ7ET9/7CMKwpEe0oMHDjQateuneymIMXO1ixcuNAaNGjAhDPkQnwgCPGBVIyN/fbbj+QhH0gg4KtKlSpWoUKFZDcDKUTL3GmN6BIlSrBTOXIhPhCE+IAfYiN8SCDgiz9ixIsJJtfDD/GBIMQH/BAb4cMqTPC1c+fOZDcBKUYT67Vjp66BWMQHghAf8ENshA8JBOI68MADORuAXLTmghJL1l5APMQHghAf8ENshA+rMCEuVl8CAAAoWg5IcBUmRiAQ165duzgTAAAAgFxIIBDXoEGD7Ndff012M5CCS+39+OOPbL6DuIgPBCE+4IfYCB+K3OFr27ZttmnTpmQ3o8hKxbWpNS+mRo0azI9BXMQHghAf8ENshA+/qTSxYcMGe+qpp2zq1Km2ZcsWO/TQQ+2OO+6wFi1a5PuYZ599tq1cubJQ24lw745ZqlQp1y4gHuIDQYgP+CE2wocEIk3cfvvttmbNGpdEaDOW0aNH21VXXWVvv/22HXLIIfk6Zu/evdlILkk0jNuzZ0+3pF0qJRC7d+92E+w1yUob/gDRiA8EIT7gh9gIHxKINKC1k7/88ksbN26cNW/e3N12//332xdffGGTJk2yW265JV/HLV26dMKz8VE0bN++3RYtWmSZmZnEBnIhPhCE+IAfYiN8mESdBipXrmzDhw+3Jk2a5NjVUZf8zmHQSIZq8IFoZcqUscMOO8xdA7GIDwQhPuCH2AgfEog0oDKjNm3a5Ojwf/jhh25kolWrVvk6phIPJSBANMWE4ozYQDzEB4IQH/BDbIQPJUxp6Pvvv7c+ffpY+/btrW3btvk6xkUXXeR2hUTyV8LS5LJU2h9k/fr1VqVKFVbLQC7EB4IQH/BDbKSOREvI+C2lmcmTJ1uvXr3smGOOsQEDBuT7OEcccYTt2bOnUNuGvNMoUrly5SzVsFM5ghAfCEJ8wA+xkXxaiCcRJBBpZMyYMda3b18744wz7PHHH2cOQxqoV6+elS9fPtnNAAAAiCCBSBNagemRRx6xrl272r333ksdYZrQEq5MKgMAAKmESdRpYPHixfbYY4/ZaaedZj169LC1a9e6PSF02bx5c7KbhzTbn+Knn35y10As4gNBiA/4ITbChxGINKAVlzTh+eOPP3aXaB07drT+/fvnay5Fp06dqEdMklT9EtXkNu0WyiQ3xEN8IAjxAT/ERvgUy87Ozk52I5B6qlWr5kYwkDz6Ms3KykqpnagBAED6SnQVJhIIxDVt2jSrVasWO0ImkSbBp1rysHv3bjc6onkZJUqUSHZzkGKIDwQhPuCH2EgdLOOKApkyZYpdfPHFVqNGjWQ3BSlk+/bttnDhQsvMzCS5RC7EB4IQH/BDbIQPCQR8sQwsYmlE5NBDDyU2EBfxgSDEB/wQG+FDAgFfxYuzSBdyx0SqlVUhdRAfCEJ8wA+xET70EOFLKzsB0Xbs2GErVqxw10As4gNBiA/4ITbChwQCcWn3Y0YgEGvPnj22ZcsWdw3EIj4QhPiAH2IjfFiFCXGx/wMAAEDRckCCk9g5xQwAAAAgYSQQiGvQoEH266+/JrsZSDFap3vOnDkpu1M2kov4QBDiA36IjfBhFSbEtWvXLveHvGnTpmQ3JSU3VCuqSpYsaZUrV3bXQCziA0GID/ghNsKH31SaWLdunfXv39+++OILtyHLsccea3fffbc1aNAg38c8++yzbeXKlZZsGRkZlpWVRRKRAkqVKmU1a9ZMdjOQoogPBCE+4IfYCB8SiDRxww03uNULhg8fbmXLlrVnnnnGunfvbh999JHbGj4/evfubRUqVLBk0ihIz5493dJuJBDJpxjbtm2b+12wShdiER8IQnzAD7ERPiQQaWDjxo120EEHWY8ePaxRo0buNnW6zz33XFuwYIE1bdo0X8ctXbo0W8ojB33BK6YyMzOJDeRCfCAI8QE/xEb4kOalgYoVK9rAgQMjycP69ett1KhRVqNGDWvYsGG+jjls2DA3pAhE09khfcEzGoR4iA8EIT7gh9gIH0Yg0sz9999vr7/+upt4/Pzzz+c7k9fcB4YREUsxwdkh+CE+EIT4gB9iI3xIINLM5Zdfbp06dbKxY8e6eRHjxo2zI444Is/HOeuss9xKTKk0vMmISPIpJjZs2GCVKlVitQzkQnwgCPEBP8RG6kg0keO3lGa8kqW+ffvarFmzbMyYMdavX788H+e4446z3bt3W6pYunSplStXLtnNwP+zZcuWZDcBKYz4QBDiA36IjeSrWrVqQo8jgUgDmvMwffp0O/300yOZu4YDlUysXr3a0kG9evWsfPnyyW4GAABAkUcCkQbWrl1rt99+u40YMcJatWrlbtu5c6fb1bFdu3aWDjSxKr/L0QIAAKDwMEs2DWj1pdatW9ujjz5q33zzjc2fP9/t4aBdpLUXBFCYc1HmzZvnroFYxAeCEB/wQ2yEDyMQaeKpp55yS7nedttttnnzZmvRooWbSF2rVq18HU8lUeecc45t3brVkr2RHFKHSuM0F4UVuhAP8YEgxAf8EBvhUyw7Ozs72Y1A6qlWrZqtWbPGUkFGRoZlZWWxPjQAAEAKrMJEAoG4fvjhB6tSpYrbjTrZtKcFyUNq2LNnj+3YscP9TjhThFjEB4IQH/BDbKQOlnFFgUycONEuvvhid/Yf8Kg+dcGCBW7HUDb9QSziA0GID/ghNsKHNA++2LgNsTQi1aBBg5QYmULqIT4QhPiAH2IjfBiBgK8SJUokuwlIwZhgQz/4IT4QhPiAH2IjfBiBQODW8kA07S+yatUqdw3EIj4QhPiAH2IjfEgg4Fu+pElNQGxSqdW5SC4RD/GBIMQH/BAb4cMqTIgr2fs/AAAAYN9KdBI7IxAAAAAAEkYCgbiGDh1qK1asSHYzkIJL7c2fP99dA7GIDwQhPuCH2AgfVmFCXH/++adt3rzZNm3aVKDjsAlcetEGPxreZKMfxEN8IAjxAT/ERviQQKSZxYsX2/nnn2/333+/uy6Ic845x1auXFmgY2gjuqysLJKINKGEsHbt2sluBlIU8YEgxAf8EBvhQwKRRrT8Wa9evQptAnTv3r2tQoUK+X7+X3/9ZT179nTb05NApAetuaA40ypdxYoVS3ZzkGKIDwQhPuCH2AgfEog0MmTIkELdiEU7QrKlPGKTwgULFlhmZiaxgVyIDwQhPuCH2Agfis3SxDfffGOvvfaa9e/fv1CON3r0aHcmAIhNKg855BB3DcQiPhCE+IAfYiN8GIFIA5rofNddd9l9991nNWvWLJRjLly4sNAmM2lVBZKR9FGyZElXlgbEQ3wgCPEBP8RGakh0BIgEIg08+OCD1qxZM+vQoUOhHbNt27aFtiPk0qVLC7W0CgAAAIWvatWqCT2OBCLkJkyYYN9++61NmjSpUI+rBGL37t2Fcqx69epZ+fLlC+VYSK7t27fbsmXLrE6dOgw1IxfiA0GID/ghNsKHBCLkxo8fb+vWrXMd/mgPPPCAvffeezZixAhLNq3AVKZMmWQ3A4VAv8dKlSoluxlIUcQHghAf8ENshA8JRMgNGDAg186N7du3t5tvvtn++c9/Jq1dAAAASE8kECFXvXp13xo2v/uA/GKYGUGIDwQhPuCH2AgfEgjENXv2bJeEFGRTOq3rjPSiDX7Y6Ad+iA8EIT7gh9gIn2LZ2v4PiFGtWjVbs2ZNgY+TkZFhWVlZ7EQNAACQJsu4kkAgLu0IqU5/Qfdv2G+//Uge0oi+LrQ6V4kSJThThFyIDwQhPuCH2AhfAsFO1Ihr3LhxtmXLFqtQoUKBLiQP6UVlaT///DPlaYiL+EAQ4gN+iI3wIYFA4K6QQOyIkvb10DUQi/hAEOIDfoiN8KGHCF8kEIgXE6zVDT/EB4IQH/BDbIQPIxDwtWvXrmQ3ASkYE9q4kNhAPMQHghAf8ENshA8JBHzxh4xYO3bssOXLl7trIBbxgSDEB/wQG+HDKkyIqyD7PwAAACB8WIUJAAAAQKFjliziGjFihLVt29ZtKJcX7PuQ3rZv326//fab1apVy0qXLp3s5iDFEB8IQnzAD7ERPiQQaWLVqlXWunXrXLf369fPzj///Dwf748//rAzzjjDVq5cmafnsfM0AABAeiOBSBPqtCtrnzx5co5dHMuXL5/vY/bu3dttBpcobQDTs2dPNwmKBCI9KcYOPvjgZDcDKYr4QBDiA36IjfAhgUgT8+fPt/r16+e55Ojv/qATnUyDokFrLuiiJDU6UQWE+EAQ4gN+iI3wYRJ1mpg3b541aNAg2c1AmtMo048//uiugVjEB4IQH/BDbIQPCUQajUCsX7/eunTpYieeeKJ17tzZPv/883wf76233mInasSdJF+3bl13DcQiPhCE+IAfYiN86CGmyYZvixYtsoYNG7p5C+XKlbN3333Xrr32Whs5cqS1bNkyz8ecPXu2lShRIl/t2bZtm5UqVSpfz0Xq0/yWnTt3ugsQi/hAEOIDfoiN1JBo6ToJRBrQSMGMGTNch9+bvHzkkUfaggUL7KWXXspXAnHsscfa7t2789WepUuXuiQGAAAA4VG1atWEHkcCkSbKli2b67bMzEybNm1avo539tlnu5GN/KhXr16BVn9C6tLo0rJly6xOnTqstIVciA8EIT7gh9gIHxKINKCRhk6dOtnzzz9vxx9/fOT2n376yZU17Wv64y9Tpsw+f13sm99txYoVWSkDcREfCEJ8wA+xET4kEGlAqy8dcsgh9vDDD9tDDz1klStXttdff91mzpxp48ePT3bzkEb4ckcQ4gNBiA/4ITbChwQiDRQvXtxeeOEFGzhwoN166622adMmO/zww90E6kaNGhVoa/mtW7cm/HiWX0t/igntTl6zZk23TwgQjfhAEOIDfoiN8CGBSBMHHnig9evXr9COp4nQgwcPdkvD5kVGRgbLsKW5PXv2JLsJSGHEB4IQH/BDbIRLsWxt/QfEUOKwY8eOPD9PyQMToAAAAMKHZVxRIKpF1FKsKo8CAAAAPPQOEZfKl7SkGhBNc2JmzZqVp7kxKDqIDwQhPuCH2AgfEggEblAHxJao1a5dm3kuiIv4QBDiA36IjfChhwhfJBCIFxOJ7lKJoof4QBDiA36IjfBhBAK+du/enewmIAVjYuPGjcQG4iI+EIT4gB9iI3xIIOBr586dyW4CUnCt7iVLlrhrIBbxgSDEB/wQG+HDMq6I6/fff3dLeVHGhGj6utAZohIlSrBrKHIhPhCE+IAfYiN8y7iSQCAuVkIAAAAoWg5IMIGghAlxjR492hYtWmSbNm1yl23btiW7SUgB2lzw119/zdcmg0h/xAeCEB/wQ2yEDwlEGpkwYYKdddZZ1qRJEzv77LPt/fffL1AJU/v27a1mzZru0rhxY5II2J49e9wXvK6BWMQHghAf8ENshA8lTGninXfesXvuucddWrVqZe+++67bDG7cuHHWrFmzPB/voYcessqVK1uFChXsr7/+sp49e9rKlSvdvwEAAJB+KGEqQpQDPvPMM9atWzfr0qWL1a1b166//no78cQT7euvv873cUuXLu0CqUyZMoXaXgAAAIQXCUQaWLx4sa1YscI6dOiQ4/aXXnrJevTokbR2If1oNOrHH39010As4gNBiA/4ITbChzU60ySB8FZOuuqqq2zOnDluS3iNQrRr1y5fx/zwww/dsaJpDkSpUqUKpc0Ip127drndQnXNFz1iER8IQnzAD7ERvhImEog0sGXLFnd9991324033mi9evVyCYDmLYwcOdJatmyZ52NOnz7drr322hy3LV261MqVK1do7UZ4acdQwA/xgSDEB/wQG8mnRC4RJBBpwBsV0IhBx44d3c+HHXaYG4nIbwJx+OGH59pSvl69ela+fPlCajXCSCtkaCRq//33t+LFqYBETsQHghAf8ENshA8JRBqoXr26u27UqFGO2xs2bGhTp07N1zEvvvhiN5QYTX/YTKgu2lQmt3z5csvMzCQWkAvxgSDEB/wQG+FDmpcGjjjiCCtbtqzNmjUrx+3z5893KzIBhUVJpEa3dA3EIj4QhPiAH2IjfBiBSAP6g7v66qvtueeec6MRTZs2dftAfPnllzZq1KhkNw9pREPL++23X7KbgRRFfCAI8QE/xEb4kECkCU2Y1rDfoEGDbNWqVdagQQMbMmSIHX/88fk+5vbt292wIisiwKOdQlevXm3VqlXjyx65EB8IQnzAD7ERPiQQaeSKK65wl8KgP+Rnn33W1q5d6/6dkZHBHzXcRDcllboGYhEfCEJ8wA+xET7FsrWNMRBj/fr17oyAR8kDtYkAAADpi30gUCBKFkgYAAAAEItVmBDXwIED7ddff012M5BiNB/m559/Zl4M4iI+EIT4gB9iI3xIIBCX6hBLlCiR7GYgxZQsWdIOPPBAdw3EIj4QhPiAH2IjfPhNwRd/yIi367m3cSEQi/hAEOIDfoiN8GEEAr52796d7CYgBWPizz//JDYQF/GBIMQH/BAb4UMCAV87d+5MdhOQYrQ3yC+//OKugVjEB4IQH/BDbIQPy7girgULFrh6xNKlSye7KUixuTFa3lfL+mrnUCAa8YEgxAf8EBvhW8aVBAJxaUMXAAAAFB0HsA8ECuKNN96wY4891qpUqZKn57HhXHrTGSLtTq7RKXYmRyziA0GID/ghNsKHBCINzJgxw7p16xb3vtq1a9uUKVPyfMwlS5ZYnz59bOXKlXl6XkZGhmVlZZFEpPEw86ZNm/KcWKJoID4QhPiAH2IjfChhSpPMfePGjTlumzlzpt10003Wt29fu+CCC/J8zIceesgqV65sFSpUSPg52gCmZ8+eLunIy/MAAACQfJQwFSEa7tOZ/+j5C/369bOOHTvmK3nwaAJ1ooEEAACAooGp7mnohRdecKMBd999d7KbgjSzbds2mzt3rrsGYhEfCEJ8wA+xET6MQKSZ9evX26hRo+yOO+6wSpUq5fs4X3zxhV1yySX5eq6+ALSrJNLPrl27rFy5cm6PEKofEYv4QBDiA36IjdRBCVMRNW7cOCtfvrx16tSpQMfRxOvLLrssX89dunSp+yJA+tqyZUuym4AURnwgCPEBP8RG8lWtWjWhx5FApJkJEybYeeedV+BVkOrXr+9WRciPevXquSQG6UdnhrzNfooVK5bs5iDFEB8IQnzAD7ERPiQQaUTLpy5btsw6dOhQ4GN1797dDSXmh5KXMmXKFLgNSD2aoK8RpszMTH7HyIX4QBDiA36IjfBhEnUa+fbbb93QU+PGjZPdFKQpJYf6gmefD8RDfCAI8QE/xEb4MAKRRubMmWOHHnpospuBNFa8eHGW9oUv4gNBiA/4ITbChwQijaxZs6ZAKy/F2r59uxtWTJSWjkV6U1nbunXr3EgXK20hFvGBIMQH/BAb4UMCkUZefPHFQl0JYejQobZ69eo8PU8b2mkSFNJ3qT0tFVyxYkW+5JEL8YEgxAf8EBvhUyybBXcRh/6QtSJCXil5oIYRAAAgfNgHAgWiJIBEAAAAALFYhQlxDRkyxJYvX57sZiDFaJfxefPmuWsgFvGBIMQH/BAb4UMCgbj4I4bfShnaZVzXQCziA0GID/ghNsKHEib4YiIT4s1xOeigg5LdDKQo4gNBiA/4ITbCh1QPvvbs2ZPsJiAFY0LL+xIbiIf4QBDiA36IjfAhgYCv/KzChPQvbcvKyqLEDXERHwhCfMAPsRE+LOOKuH744QerW7eulSlTJtlNQQrZvXu32zBQcVGiRIlkNwcphvhAEOIDfoiN8C3jSgKBhPaBYH8HAACA9JZoAkEJUxrt4vjMM8/YySefbM2aNbMuXbrYzJkz83287t27W6NGjaxmzZru0rhxY4YWYTt37nS7k+saiEV8IAjxAT/ERviwClOaeP755+2NN96w/v37W506dezFF1+0q6++2t577z2rVq1ano/XvHlzO/XUU61ChQpuWLFnz55uRIJRiKJNiaq+5MuXL88qXciF+EAQ4gN+iI3wYQQiTUyePNnOOecc+8c//mH16tWz3r172+bNmws0ClG6dGk3lMU8CHgUC0ceeSQxgbiIDwQhPuCH2AgfEog0UbVqVfv000/d7tGajPTaa6+5eQsqPQIAAAAKCwlEmrj33nvdsN8pp5xiTZo0sUGDBtngwYPdSkpAYdE8mAULFjAfBnERHwhCfMAPsRE+zIFIE7/88ourHXzuueesevXqbj5Er169bMyYMXbYYYfl+Xjff/+9dejQIcdt+sOmNrFoU52qYkAT3VjADbGIDwQhPuCH2EgdLONahKxcudJOO+00GzVqlLVo0SJy+6WXXmqVKlWyoUOH5vmYxYoVs5EjR7pA2rp1q11xxRU2depUK1euXCG3HgAAAKlAi+gkghGINDBr1iyXtat0KdpRRx1ln3/+eb6OmZGRkWtLeU3O1igHijadKSpZkq8OxEd8IAjxAT/ERrjwm0oDNWrUcNfz5s2zpk2bRm6fP3++1a9fP1/HvOGGG3Ktx6wlXFkhoWjTaNSiRYssMzMz4WFOFB3EB4IQH/BDbIQPk6jTgJIGDTndfffd9tVXX9mSJUvs6aeftunTp9u1116b7OYhjWhp34MPPthdA7GIDwQhPuCH2AgfRiDSQPHixd1Gckoa+vTpYxs3bnS7SGtOhMqY8mv79u3urIA2kgOkRIkSbnNBIB7iA0GID/ghNsKHSdSI66GHHrJhw4a5CdrenIisrCx2oi7iVKO6YcMGNzmfWlXEIj4QhPiAH2IjdSRaQsZvCb6rME2cONFq167t/q1N6UgesGPHDvvtt9/cFwxf8ohFfCAI8QE/xEb4MAKBuFS6BAAAgKLjgARHIJhEDQAAACBhJBCIa/jw4ZH5D0D0xPqFCxe6ayAW8YEgxAf8EBvhQwKBuLSS0+7du5PdDKTg3BjVp+oaiEV8IAjxAT/ERvgwUwW+SpUqlewmIMVoMr12JAfiIT4QhPiAH2IjfBiBgC/m1yNeTGhkithAPMQHghAf8ENshA8JBHxRi4hY2lTwp59+YnNBxEV8IAjxAT/ERviwjCvimjZtmtvNuly5csluClJss58tW7a4uGCtbsQiPhCE+IAfYiN8y7iSQCCu9evXu41dPGwkBwAAkN7YB6KIUeb+wAMP2D/+8Q877rjjrFevXrZu3bp8H69Tp07WsGFDq1mzprs0btzYtm3bVqhtRjjPEim51DUQi/hAEOIDfoiN8CGBSBO33HKLffbZZ9a3b18bO3asqyPs1q1bjlGEvFAi8thjj9nIkSNt6NChtmbNmnwfC+lDMbBs2TJiAXERHwhCfMAPsRE+JBBpYO7cuW7OwsMPP2xt2rSxzMxMe+KJJ2z16tX27rvv5vu4pUuXdkNZZcqUKdT2IrwUD02bNk14iBNFC/GBIMQH/BAb4UMCkQaWLFnirlu0aBG5rWzZsm5N5a+//jqJLUM6YqMfBCE+EIT4gB9iI1yY6p4GqlWr5q5XrlxpDRo0cD9rPeXff//dqlatWmivozkQbC5XtO3cudOVs2VkZBALyIX4QBDiA36IjdSR6CgQCUQaaNKkiR1yyCFuEvXAgQOtYsWKNnjwYPvjjz/cH2V+zJs3z2rVqpXjtqVLl7KsK5wVK1YkuwlIYcQHghAf8ENsJF+iJ55JINKAllh99tln7a677rLWrVu77L1Dhw528sknW/Hi+atSe/XVV619+/Y5blNJVPny5Qup1QAAAAgjEog0odKl8ePH24YNG9wmLBopuPDCC+2EE07I9xBW7BYh2geCCdVQXFCrCj/EB4IQH/BDbIQLk6jTZA+Iyy67zLKysqxSpUoueVi+fLnNmTPHTjrppHwdU6MZLKeGWFu3brXZs2e7ayAW8YEgxAf8EBvhQwKRBpQwKHPXHhALFiywH3/80a6//no3+tCyZctkNw9pVi5Xp04ddw3EIj4QhPiAH2IjfEgg0sRTTz3lJk937tzZevToYc2bN7chQ4YU6Jjbt293ZwO0KR0gKo+rUqWKuwZiER8IQnzAD7ERPvym0kT16tXdROrC1L9/f7c0rGhpNc4MYNeuXa5kTqNefNEjFvGBIMQH/BAb4cMIBHxNnDjRJRC6aH6FJlGjaNO8GC3ny/wYxEN8IAjxAT/ERvgUy45dagcws7Vr11rp0qWtRIkSyW4KUoi+Lvbs2eOWB2a1DMQiPhCE+IAfYiN8G8mRQCAuVkIAAAAoWg5IMIGghAlxvfLKK7Zq1apkNwMphmFmBCE+EIT4gB9iI3xIIBCXkgf+kBFLA5aa7MbAJeIhPhCE+IAfYiN8mOoOX6y6hFiaF6Ndz4F4iA8EIT7gh9gIH0YgAAAAACSMBAK+tm3bluwmIAUn18+ePZtJ9oiL+EAQ4gN+iI3wIYFAXCeccIJVrlw52c1ACpa11apVi/I2xEV8IAjxAT/ERviwjCviWr9+/V6ZRK0vBzakAwAAKIL7QAwbNsymTZtmo0ePznH73LlzrW/fvvbTTz9ZlSpVrHv37tatW7c8Hbtr16520EEHWf/+/fPTNBSCk046yX744Qf766+/CvW4GRkZ7GodYrt377Y///zTypYtyyaDyIX4QBDiA36IjfAlEPlahWns2LH29NNPW4sWLXLc/scff9gVV1xh7dq1s4ceeshmzpzprhUQF1xwQX5eCknSvn1769Spk1WoUKHQjqlkpGfPnm5kgwQinLZv326LFy+2zMzMhL9kUHQQHwhCfMAPsRE+JfO6N8ADDzxgM2bMsPr16+e6//XXX7dSpUrZww8/bCVLlnRLcmljkOHDh5NAhHRZNf6QEa1MmTJ2+OGHu79vIBbxgSDEB/wQG2k+ifrnn392CcLEiRPtqKOOynX/t99+a8cdd1yOANBk3CVLltjatWvjHlNnox977DFr2bKlNW/e3J588knbs2dPjsdMnjzZLrroIjv66KOtSZMmdv7559sXX3wRua9x48a2YsWKHM/R2fPHH3/c/TxhwgQ7++yz3XNbtWrlSqyC6vtXrlxpvXr1cmU8es2rrrrKld14evfubTfffLNdeeWVdswxx9iLL76Y6xhKsvTH8PHHH9vpp59uTZs2daVcOvajjz7qRm/0np9//vkcz1Nb//nPf7rHayRn6NChbmhPli9fboceeqgrH1PbTjnlFNuyZYtt3rzZ7r//fvdZ6zPU6/z444++7w/Ir2LFirnvAF0DsYgPBCE+4IfYCJ88pXrq0Ori5/fff7dGjRrluK1atWruWh3nAw88MNdz1Jn+5JNP3HwHzcB/4YUXXCJSp04dd7/mUtx000129913RzrMAwcOtLvuuss+++wza9u2rZtr8c4777jyGNEwmMqnlCio43/ffffZgAEDXKd84cKFdscdd7gVhrzHR9PxO3fu7F5fnXtN+h0yZIhddtll7jU0N0M+/PBDu/POO13H3a8cRx1/HUOvrR0We/ToYeeee64bjXnjjTdcIqZSMH2mSgxGjRrl3psSFCUIs2bNcqM5Kg279957I8d9++237d///rcrCVJ5mNqrNiixKFeunGunbtOIkJKYVFweVl8UCB/F8bp166xq1aqcKUIuxAeCEB/wQ2wUkTkQQR3D2CW4VAbj1bfF66y/9dZbriyqTZs27jaNRnz11VeRx2gyjTrpl156aeQ2nWG/5pprXLDVrFnTdcqjEwidxddoQ8OGDd0IhTJadfyVoOjy0ksvuY52POrUq8OudikxEXXqTz31VDf3Q4mLVKxY0a6++uq//UxuueUW1xbRCIGSAh1DbVJCoRGGBQsWuMRLIxlKVLp06eIerzKxDRs2uFEZjXh49Fnovcn06dNdsqTPrFKlSu6222+/3b7//nt75ZVX8j0RfdmyZe6z2htU1ub3+SMcNBoG+CE+EIT4gB9iI/mUxO3zBEJnwWNLg7zEIV5Go5GCnTt3RjrYXsIRfdb8sMMOc511zaNYtGiR63x65UReaY/O6L/88suuc65RBiUBSjBEJUvNmjWzCy+80GrXrh0p/TnyyCPjvof58+e7jruXPHjvS8fVfZ569eol9JlEP06fgdrgDdF5Ixf6zLRsqsq8VIIUTSVh+oz03r1favQxVVamhbROPvnkHM/TMeMlbYlSkvWPf/zD9ga1v3z58nvl2AAAANi7CjWBqFGjhq1evTrHbd6/q1evnuvxXkc6diXZ6OGrr7/+2s1BUKmSOtcdOnRwpTs33HBD5DE6G685GUocNAqijvg555wTSUh0Jn7OnDlu2VldrrvuOjvvvPOsX79+udrkt6qt5mVEtyvRVYRih+KKF48/7STodWOPE/3aul9n8zViEitVN2RR+zVhCgAAAEV8J+pjjz3Wvvvuu8jIgKi05uCDD447JKLb1cFXuU10HVz0hGWNLBx//PFuHoL2lNAIguZTxHa6NQqhcqUPPvjAlRt5y49qnsSzzz7rRjWuvfZal0yoHOi9996L+x40F0GTvlUe5dGZfM3F8MqG9gbND9FFn180zQfRfIG6devGfZ5Kn1QKplEKndn3LiqHmjJlSr7b8+CDDxZoBAPpScm7/hYKe38QpAfiA0GID/ghNop4AqFOvDqzmvD7yy+/uLPimhisWv94NAFYNf+DBw+2jz76yE1w1nwILRfr0RyHefPmuY60auPGjx9vzzzzjLsvulxKqyxt3LjRvWbHjh0jt6vz/dxzz7l2qK5fATp16lRX1hSPRjg0l+DWW2+12bNnu2RGKzJt3brVrey0N2mkZcyYMTZu3DhXqjVp0iSX/Oh1/Up+VKKlMq/bbrvNJWt6nkZW9DloGV2gMGkkTAsjMMkN8RAfCEJ8wA+xET6F+pvSKMOIESPc6kfqxGvXYU0Yju7Qx9KKSBqF0GpD2oXwzDPPzLHSk0YLVJKksiPRKIAmWmsFJC1V6nWSVcajkQeVPGmUwnPiiSe69mgkY9CgQa58RhO2tdJRPOqoqxOvycca8RCVTr366quRlaH2Fi0Lq7IjrbCk96iSMM3lUGLhR5PM9d400VpJj7J3fSZKPLRMbEFoBEKJU2HhzEL4KSH3VlYDYhEfCEJ8wA+xET7Fsv2K70Ooa9eubl8GnY1HwWgHcS0L65WLFRYllRrVYSfqcFJ5ohJBzWFR8gpEIz4QhPiAH2KjiC/jmiya+zB37ly3nOkTTzyR7OakDU1K16pRhUkjLCQP4aVRKZUaZmZmsks5ciE+EIT4gB9iI3zSYgTikksucUvC9unTx62uhILT56nN9lJ1JSckh1b90oR9DTf7rSiGoov4QBDiA36IjdSRaAKXFgkECl9hzn0AAABA+iQQpHmI64033rA1a9YkuxlIMVr5bMWKFbk2jASE+EAQ4gN+iI3wIYFAXNoLg1WTEG+YWUs1exscAtGIDwQhPuCH2AiftJhEjb1Dy+sC0TQBXpstAvEQHwhCfMAPsRE+jEAAAAAASBgJBHxt27Yt2U1AilFZ25w5cyhvQ1zEB4IQH/BDbIQPqzABAAAASBgjEAAAAAASRgIBAAAAIGEkEAAAAAASRgIBAAAAIGEkEAAAAAASRgIBAAAAIGEkEAAAAAASRgIBAAAAIGEkEAAAAAASRgIBAAAAIGEkEAAAAAASRgIBAAAAIGEkEIjYs2ePDR482Fq1amVHH320XXPNNbZs2bJkNwspYNiwYda1a9cct82dO9cuu+wyFyvt2rWzV155JWntw761YcMG+9e//mWtW7e2Y445xjp37mzffvtt5P7p06fb+eefb0cddZSdccYZ9u677ya1vdi31q1bZ3feeaedcMIJ1qxZM7v22mtt4cKFkfv57oAsXrzYxcdbb70VuY3YCA8SCEQMHTrUxo0bZ4888oj95z//cQnF1VdfbTt27Eh205BEY8eOtaeffjrHbX/88YddccUVVrduXRs/frzdcMMNNmDAAPcz0t/tt99uP/zwgz311FPud37YYYfZVVddZYsWLXIdxR49ergTEeoYXHTRRXbXXXe5pAJFg74Pli5dasOHD7c333zT9t9/f+vevbv99ddffHfA2blzp/Xq1cu2bt0auY3YCJeSyW4AUoOShJdfftn9Qbdt29bdNmjQINcJ+Oijj+ycc85JdhOxj61atcoeeOABmzFjhtWvXz/Hfa+//rqVKlXKHn74YStZsqQ1aNAg0mG44IILktZm7H36PX/55ZfuZEPz5s3dbffff7998cUXNmnSJHf2+dBDD7XbbrvN3afYmDNnjo0YMcJatmyZ5NZjb9u4caMddNBBLols1KiRu61nz5527rnn2oIFC1wiyXcHhgwZYuXKlctxG/+vhAsjEHCysrLszz//zPEffIUKFezwww+3b775JqltQ3L8/PPP7st84sSJrhQlmspVjjvuOPcl71G5wpIlS2zt2rVJaC32lcqVK7v/0Js0aRK5rVixYu6yadMmFxuxiYJi47vvvrPs7OwktBj7UsWKFW3gwIGR5GH9+vU2atQoq1GjhjVs2JDvDrg+xWuvvWb9+/fPcTuxES4kEHB+//13d12zZs0ct1erVi1yH4oW1Z/qLFGdOnVy3aeYUIcgNlZk5cqV+6yN2Pd0YqFNmza23377RW778MMP3ZlCjVj6xYZXvoKiQyNTSiY1B6Zv3752wAEH8N1RxOkkg0oa77vvvlz9DWIjXEgg4Og/d4nuFEjp0qVt+/btSWoVUtW2bdvixooQL0XL999/b3369LH27du78sd4seH9m/lURcvll1/u6tdVAqt6do1q8t1RtD344INu4nSHDh1y3UdshAtzIOBokpv3H7z3s/dHW6ZMmSS2DKlIMRLbGfS+4HWWEUXD5MmT3bwprcSkyY7ef/ixseH9m++SokUlS6LRh1mzZtmYMWP47ijCJkyY4MqUNFcqHmIjXBiBgOMNJa5evTrH7fp39erVk9QqpCoNM8eLFSFeigZ1Bm+66SY7+eST7YUXXoicKdR3SbzYUAegfPnySWot9hXNeVDJ0q5duyK3FS9e3CUTigO+O4oujUZpkQWNVGoUQhfRYh1a8ZHYCBcSCDiNGzd2KyJoxZ3oWkWtnnLssccmtW1IPYoJTYrdvXt35LavvvrKDj74YKtatWpS24a9z1vuuUuXLm4p1+iygxYtWtjXX3+d4/GKDY1SqCOJ9KbJrlrmN3rZXi3Zqf9LtKoO3x1Fl0Yp33vvPTcS4V3k5ptvdqNUxEa48G0ORx0Abd6iP/ApU6a4VZm0DKPOCKi2GYimJfW2bNli9957r/3yyy9uvX+ttKKlG5H+mz899thjdtppp7nftzqMa9ascZfNmze7DQdnz57tvku0J4SWh/7ggw/cGUakP62+pA0GH330Ubfazvz58613797uhJT2guC7o+jSKEK9evVyXETJge4jNsKlWDbr6uH/Udavs4n6o9VkJp0N0G6ztWvXTnbTkGTqAKxYscJGjx4duU2dRJ010pnFjIwMu/LKK10SivSmciXtERNPx44d3dKMn3/+uT355JNu+UV9f6jU6ayzztrnbUVyKJHUUq6aI6OfNSql75DMzEx3P98d8GjPmH79+rmd64XYCA8SCAAAAAAJo4QJAAAAQMJIIAAAAAAkjAQCAAAAQMJIIAAAAAAkjAQCAAAAQMJIIAAAAAAkjAQCAAAAQMJIIAAAAAAkjAQCAFAg7dq1czvKepfGjRvbMccc43aQ/eabbyzV7dy500aNGmWp4tFHH420Z9myZdapUydr1qyZXXfddbZp06bI4/bs2WPnnXeeffbZZzme/9dff7mdv3/77bd93nYARQMJBACgwK688kqbNm2au3z++ef2n//8x8qVK2dXX311yndk//vf/1q/fv0sFXz33XfuM+zSpYv795NPPmkNGjSwCRMmuERn+PDhkcdOnDjRKlSoYG3atMlxjDJlyrjP/b777tvn7QdQNJBAAAAK7IADDrCMjAx3qVatmjVq1Mgeeugh27Ztm3388ceWyrKzsy1VDBo0yLp27WqlSpVy//7ll1/szDPPtHr16tmpp55q8+fPd7dv377dBg8ebHfeeWfc45x77rk2b948mz59+j5tP4CigQQCALBXlCxZ0l3vt99+7nrHjh3ujHqrVq1cSc7FF1/szrZ73nrrLTvttNNcCU/z5s2tZ8+e7valS5fa9ddf7247/vjj7fbbb7d169ZFnjd+/HjXyW7atKm7/ve//+3Ke2T58uWurOrDDz+0iy66yI488khXcvXaa69FXrNPnz7uZz1uxowZ7rnDhg2z008/3T1e5Vg6o//rr79GXnP9+vV22223WYsWLVybBgwYYN26dbMhQ4ZEHvPpp5/a+eef79ql9/X000+7z8DP7Nmz3QiEXtdTu3Ztd5vapOuDDjrI3f7KK6/YUUcdZU2aNIl7rBIlSrjjjBw5Mo+/NQD4eyQQAIBCt2rVKnv44YfdyIRXYqOO+pdffuk622+//bbr7Kuuf+rUqZHnqZO+evVqV7KjDrpq/lXOo463EgN1iPWYW2+91T1eicATTzxhN954o7377rvu9hdffNG9RjSVKOm13n//fWvbtq09+OCDbn6B5grcc8897jFKZpTYqHP+0ksvWe/evV3i8dxzz9mSJUusf//+7nHqzPfo0cMlNiNGjLCXX37ZZs6caV9//XXk9VTGpbYoSVKJ1AMPPOBe22/EQKZMmWJHHHGEHXjggZHbdAwlSEpk5s6d6153w4YN7nNQIhVE7/N///ufmxMBAIUqGwCAAjj55JOzjzjiiOyjjz7aXY488sjsRo0aZZ955pnZU6dOdY9ZsmSJu23OnDk5nnvXXXdlX3bZZe7n8ePHu8fMnTs3cv+rr77qjrlhw4bIbbp/wIAB2du3b89u3bp19siRI3Mc880338xu0qRJ9rZt27KXLVvmjhn9mE2bNrnbJk2alON1PVOmTMn+5JNPchzzySefzD7llFPcz9OnT3ePX7hwYeT+NWvWuNccPHiw+3fnzp2zH3300RzH8J6nNsXTrVu37D59+uS6fdeuXdmrV6+O/Puxxx7LfuSRR9z70+enz+Cmm27K3rhxY47nrVy50r3eN998E/f1ACC//u/4MgAABXDJJZe42n0pXry4VapUycqXLx+5f86cOe760ksvzfE8TQzWROBo9evXj/ysmn/9u2LFipHbtMqTLioj+v333+2pp56yZ555JnK/Rgg0R0DlS6VLl3a3aSKyx2uXXjselTjNmjXLHXPx4sXuorkI1atXj7wXteeQQw6JPEejBgcffHCO96uSpDfffDPXXIuFCxe60qRYa9eudeVO8cqRNLdE9J40eVqjLePGjXOjEZpjorKvZ599NjKaIlWqVHHXa9asifs+ASC/SCAAAAWmDrUm+vrxOs9jx461smXL5rhPCUe0/fffP9c8ini8eQ4qjTrxxBNz3V+zZk1XDhU9DyNem2JppSOVLXXs2NFatmxp3bt3d+VF6rR7HXrvtYPapnkTOkYsLxmIpc9h9+7dfzvJWnMtlByoZKp169buvZ1yyik5kijxjhX7+QJAQfGtAgDY6zIzMyNnw5VoeBdNYtbFT8OGDd38g82bN0du+/nnn13HXiMI6khrLkP0MXW/JiwnqlixYjn+/cILL9gNN9zg5kloD4ajjz7atcFLODT6ofZoJMHzxx9/uDkR0e9XIxfR7dJoieZr/Pnnn3HbocRCx/Hz008/uX01lNB4iYGXyOiziE1qvInmWhULAAoTCQQAYK9Th/rkk092k4k/+eQT1+nXZGetdlS3bl3f53Xo0MGNbmjycVZWlutE6xhaJlYjDNdcc42NHj3axowZ4yZXq5xHHX+NYsQbdYhHE71Fx9ayszquJnurbGnRokXurP9HH30UWUFJqy5pBaS77rrLTZ5Wu3r16uUmK3vJiNqlCdgqK1IioeVUNVKixMNvBELlS0p+/GgFq5tuusnt8yBKbCZNmuTaqInWWi0qmsqoVMKl1aUAoDCRQAAA9gl1xNu3b2//+te/3OpHWmmpb9++cct8POosa0WkXbt2uXkWKgvSqIQ3wqAN7LRakhIIHVPH08pH2oMiUSeccIJLCHR8Lb2qUQIlEhdccIHbTVvzMHQ8ndH3NsXTcq01atRwowGXX3656/zXqlUrsn/DGWec4d7v5MmTXRKkBOgf//iHSyj8aJ+HBQsWuLkdsbTbtEZvtCysR6tTqQ0XXnih+3y0ElU0LUmr0i4vQQKAwlJMM6kL7WgAAKQ5dfA1yVoJgZcwaHRCIxMaHTnvvPPyfezOnTu7ROKqq64qUBvVHu23oSQm3vwQACgIRiAAAMgDTezWHhUDBw508x5U6qTEQSVTmtRcENr3QasrBW04lwiN7qjMi+QBwN7ACAQAAHn01VdfuTKqefPmucnMmn+geRCFMd9A5VJa5jW/oxBbt251ZWHa5K5OnToFbg8AxCKBAAAAAJAwSpgAAAAAJIwEAgAAAEDCSCAAAAAAJIwEAgAAAEDCSCAAAAAAJIwEAgAAAEDCSCAAAAAAJIwEAgAAAEDCSCAAAAAAWKL+D377xuEkXBbdAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lightweight installer for missing packages \n",
    "try:\n",
    "    import pandas as _pd\n",
    "    import matplotlib as _mpl\n",
    "    import seaborn as _sns\n",
    "    import pyreadstat as _prs\n",
    "except Exception:\n",
    "    import sys, subprocess\n",
    "    subprocess.check_call(\n",
    "        [\n",
    "            sys.executable, \"-m\", \"pip\", \"install\", \"-U\",\n",
    "            \"pandas\", \"matplotlib\", \"seaborn\", \"pyreadstat\"\n",
    "        ]\n",
    "    )\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns  # optional; only needed if you want seaborn styling\n",
    "\n",
    "# Optional: seaborn style (comment out if you prefer pure matplotlib)\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Load dataset\n",
    "\n",
    "file_path = Path(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Mutiny_Conflict Paper_All\\Replication File\\jcr_mutiny_conflict_data.dta\"\n",
    ")\n",
    "\n",
    "try:\n",
    "    data = pd.read_stata(file_path)\n",
    "except Exception as e:\n",
    "    print(\"Error reading Stata file:\", e)\n",
    "    raise\n",
    "\n",
    "print(\"Data loaded. Shape:\", data.shape)\n",
    "print(data.head())\n",
    "\n",
    "# Titles for each panel =\n",
    "titles = {\n",
    "    \"mutiny_t\": \"Mutiny\",\n",
    "    \"violent_t\": \"Violent Mutiny\",\n",
    "    \"size_t_b\": \"Large Mutiny\",\n",
    "    \"duration_t_num\": \"Mutiny Duration (Days)\",\n",
    "}\n",
    "\n",
    "# Filtered datasets for each panel \n",
    "filtered_data = {\n",
    "    \"mutiny_t\": data.copy(),\n",
    "    \"violent_t\": data[data[\"mutiny_t\"] == 1].copy(),\n",
    "    \"size_t_b\": data[data[\"mutiny_t\"] == 1].copy(),\n",
    "    \"duration_t_num\": data[data[\"mutiny_t\"] == 1].copy(),\n",
    "}\n",
    "\n",
    "# Recode duration into groups \n",
    "def categorize_duration(x):\n",
    "    if pd.isnull(x):\n",
    "        return None\n",
    "    elif x == 0:\n",
    "        return \"Less than 1 day\"\n",
    "    elif 1 <= x <= 10:\n",
    "        return str(int(x))\n",
    "    else:\n",
    "        return \"10 days or more\"\n",
    "\n",
    "duration_data = filtered_data[\"duration_t_num\"]\n",
    "duration_data[\"duration_group\"] = duration_data[\"duration_t_num\"].apply(categorize_duration)\n",
    "filtered_data[\"duration_t_num\"] = duration_data\n",
    "\n",
    "# Plot settings \n",
    "bar_color = \"#a9a9a9\"  # grey\n",
    "plot_vars = [\"mutiny_t\", \"violent_t\", \"size_t_b\", \"duration_t_num\"]\n",
    "\n",
    "# Plot loop \n",
    "for var in plot_vars:\n",
    "    df_plot = filtered_data[var].copy()\n",
    "    fig, ax = plt.subplots(figsize=(8, 3.5))\n",
    "    ax.set_facecolor(\"#f5f5f5\")\n",
    "\n",
    "    # Binary indicators\n",
    "    if var != \"duration_t_num\":\n",
    "        # Recode 0/1 to No/Yes\n",
    "        df_plot[var] = df_plot[var].map({0: \"No\", 1: \"Yes\"})\n",
    "        counts = (\n",
    "            df_plot[var]\n",
    "            .dropna()\n",
    "            .value_counts(normalize=True)\n",
    "            .sort_index()\n",
    "            * 100\n",
    "        )\n",
    "    # Duration categories\n",
    "    else:\n",
    "        counts = (\n",
    "            df_plot[\"duration_group\"]\n",
    "            .dropna()\n",
    "            .value_counts(normalize=True)\n",
    "            .sort_index(\n",
    "                key=lambda idx: idx.map(\n",
    "                    lambda val: (\n",
    "                        0\n",
    "                        if val == \"Less than 1 day\"\n",
    "                        else (11 if val == \"10 days or more\" else int(val))\n",
    "                    )\n",
    "                )\n",
    "            )\n",
    "            * 100\n",
    "        )\n",
    "\n",
    "    # Handle case with no usable data\n",
    "    if len(counts) == 0:\n",
    "        print(f\"Warning: {var} has no data to plot. Skipping.\")\n",
    "        plt.close(fig)\n",
    "        continue\n",
    "\n",
    "    y_labels = counts.index.tolist()\n",
    "    y_positions = list(range(len(y_labels)))\n",
    "\n",
    "    ax.barh(\n",
    "        y=y_positions,\n",
    "        width=counts.values,\n",
    "        height=0.5,\n",
    "        color=bar_color,\n",
    "        edgecolor=\"black\",\n",
    "        linewidth=0.8,\n",
    "    )\n",
    "\n",
    "    ax.set_yticks(y_positions)\n",
    "    ax.set_yticklabels(y_labels)\n",
    "    ax.set_xlim(0, max(counts.values) + 5)\n",
    "    ax.set_xlabel(\"Percentage (%)\", fontsize=11)\n",
    "    ax.set_ylim(-0.7, len(y_labels) - 0.3)\n",
    "    ax.invert_yaxis()\n",
    "\n",
    "    # Title and formatting\n",
    "    ax.set_title(titles[var], fontsize=12, loc=\"left\", weight=\"bold\")\n",
    "    ax.axvline(0, color=\"gray\", linestyle=\"--\", linewidth=1)\n",
    "    ax.grid(True, axis=\"x\", linestyle=\":\", color=\"gray\", alpha=0.4)\n",
    "    for spine in [\"top\", \"right\", \"left\", \"bottom\"]:\n",
    "        ax.spines[spine].set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  }
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